--- a
+++ b/Projects/Caffe/allCNN/Info.py
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+############################################################################################
+#
+# The MIT License (MIT)
+# 
+# Peter Moss Acute Myeloid/Lymphoblastic Leukemia AI Research Project
+# Copyright (C) 2018 Adam Milton-Barker (AdamMiltonBarker.com)
+# 
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+# 
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+# 
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+#
+# Title:         Caffe Acute Lymphoblastic Leukemia CNN Info
+# Description:   Used to view info Caffe Acute Lymphoblastic Leukemia CNN
+# Configuration: Required/Confs.json
+# Last Modified: 2019-03-10
+#
+############################################################################################
+
+import os, sys, cv2
+sys.path.append('/home/upsquared/caffe/python')
+import caffe
+
+import numpy as np
+
+from Classes.Helpers import Helpers
+
+class allCNN():
+
+    def __init__(self):
+
+        """
+        Sets up all default requirements and placeholders 
+        needed for the Caffe Acute Lymphoblastic Leukemia CNN.
+        """
+        
+        self.Helpers = Helpers()
+        self.confs = self.Helpers.loadConfs()
+        self.logFile = self.Helpers.setLogFile(self.confs["Settings"]["Logs"]["allCNN"])
+        
+        self.Helpers.logMessage(self.logFile, "allCNN", "Status", "Init complete")
+
+    def loadCaffeNet(self):
+
+        """
+        Loads the Caffe network using prototxt layer definition.
+        """
+        
+        self.net = caffe.Net(self.confs["Settings"]["Classifier"]["Caffe"]["layerFile"], caffe.TEST)
+        
+        print("")
+        self.Helpers.logMessage(self.logFile, "allCNN", "Status", "Caffe net initialized")
+
+    def printDetails(self):
+
+        """
+        Prints and logs input, blob and parameter info.
+        """
+
+        # Prints the Net Inputs
+        self.Helpers.logMessage(self.logFile, "allCNN", "Net Inputs", str(self.net.inputs))
+        
+        # Prints the Net Blobs
+        self.Helpers.logMessage(self.logFile, "allCNN", "Net Blobs", str(self.net.blobs))
+        
+        # Prints the Net Blob shapes
+        self.Helpers.logMessage(self.logFile, "allCNN", "Net Blob shapes", str([(k, v.data.shape) for k, v in self.net.blobs.items()]))
+        
+        # Prints the Net Params
+        self.Helpers.logMessage(self.logFile, "allCNN", "Net Params", str(self.net.params))
+        
+        # Prints the Net Params shapes
+        self.Helpers.logMessage(self.logFile, "allCNN", "Net Params", str([(k, v[0].data.shape, v[1].data.shape) for k, v in self.net.params.items()]))
+        
+        print("")
+
+    def writeOutputImages(self, image):
+
+        """
+        Writes the output images for each neuron in the first convolution layer.
+        """
+
+        # Transposes the input (50,50,3) -> (3,50,50)
+        inp = np.transpose(cv2.imread(image))
+
+        # Reshape the data blob
+        self.net.blobs['data'].reshape(1, *inp.shape)
+        self.net.blobs['data'].data[...] = inp
+        
+        # Passes the input data through the network to compute the output
+        self.net.forward()
+
+        # Loops through each neuron in the first convolution layer and saves the images in that neuron
+        for i in range(30):
+            cv2.imwrite(self.confs["Settings"]["Classifier"]["Data"]["dir"] + self.confs["Settings"]["Classifier"]["Info"]["outDir"] + 'conv1/out_' + str(i) + '.jpg', 
+                        255 * self.net.blobs['conv1'].data[0,i])
+
+        # Loops through each neuron in the second convolution layer and saves the images in that neuron
+        for i in range(30):
+            cv2.imwrite(self.confs["Settings"]["Classifier"]["Data"]["dir"] + self.confs["Settings"]["Classifier"]["Info"]["outDir"] + 'conv2/out_' + str(i) + '.jpg', 
+                        255 * self.net.blobs['conv2'].data[0,i])
+
+        self.Helpers.logMessage(self.logFile, 
+                                "allCNN", 
+                                "Output Images", 
+                                "Output images written to " + self.confs["Settings"]["Classifier"]["Data"]["dir"] + self.confs["Settings"]["Classifier"]["Info"]["outDir"])
+
+    def saveCaffeNet(self):
+
+        """
+        Saves our Caffe network.
+        """
+
+        self.net.save(self.confs["Settings"]["Classifier"]["Model"]["file"])
+        
+        self.Helpers.logMessage(self.logFile, 
+                                "allCNN", 
+                                "Status", 
+                                "Caffe net saved")
+
+allCNN = allCNN()
+
+def main(argv):
+
+    if(len(argv) < 1):
+
+        """
+        Incorrect arguments size.
+        """
+
+        allCNN.Helpers.logMessage(allCNN.logFile, 
+                                  "allCNN", 
+                                  "Arguments", 
+                                  "Please provide NetworkInfo or Outputs argument")
+
+    elif argv[0] == "NetworkInfo":
+
+        """
+        Provides information about our Caffe network.
+        """
+
+        allCNN.loadCaffeNet()
+        allCNN.printDetails()
+
+    elif argv[0] == "Outputs":
+
+        """
+        Plots the outputs of each neuron as images.
+        """
+
+        allCNN.loadCaffeNet()
+        allCNN.writeOutputImages(allCNN.confs["Settings"]["Classifier"]["Data"]["dir"] + allCNN.confs["Settings"]["Classifier"]["Data"]["dirTest"] + allCNN.confs["Settings"]["Classifier"]["Info"]["testImage"])
+
+    elif argv[0] == "Save":
+
+        """
+        Saves our Caffe network.
+        """
+
+        allCNN.loadCaffeNet()
+        allCNN.saveCaffeNet()
+
+if __name__ == "__main__":
+	main(sys.argv[1:])
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